Abstract
Near-equiatomic, multi-component alloys with disordered solid solution phase (DSSP) are associated with outstanding performance in phase stability, mechanical properties and irradiation resistance, and may provide a feasible solution for developing novel uranium-based alloys with better fuel capacity. In this work, we build a machine learning (ML) model of disordered solid solution alloys (DSSAs) based on about 6000 known multi-component alloys and several materials descriptors to efficiently predict the DSSAs formation ability. To fully optimize the ML model, we develop a multi-algorithm cross-verification approach in combination with the SHapley Additive exPlanations value (SHAP value). We find that the ΔSC, Λ, Φs, γ and 1∕Ω, corresponding to the former two Hume − Rothery (H − R) rules, are the most important materials descriptors affecting DSSAs formation ability. When the ML model is applied to the 375 uranium-bearing DSSAs, 190 of them are predicted to be the DSSAs never known before. 20 of these alloys were randomly synthesized and characterized. Our predictions are in-line with experiments with 3 inconsistent cases, suggesting that our strategy offers a fast and accurate way to predict novel multi-component alloys with high DSSAs formation ability. These findings shed considerable light on the mapping between the material descriptors and DSSAs formation ability.
Highlights
Uranium alloys are considered as the primary nuclear fuel material for research and future commercial reactors owing to a combination of attractive properties, e.g., high thermal conductivity and fission atomic density, easy fabrication, and good compatibility with fuel cladding [1, 2]
Using these models trained by the four machine learning (ML) algorithms as discussed above, we predict disordered solid solution alloys (DSSAs) formation ability for 375 uranium-bearing equiatomic alloys
The 5 most important parameters, ΔSc, Λ, Φs, γ, and 1∕Ω, affecting disordered solid solution phase (DSSP) formation are determined through the analyses of SHAP values. 190 out of 375 Ubearing alloys are predicted to be DSSAs
Summary
Uranium alloys are considered as the primary nuclear fuel material for research and future commercial reactors owing to a combination of attractive properties, e.g., high thermal conductivity and fission atomic density, easy fabrication, and good compatibility with fuel cladding [1, 2]. A commercial U-50 wt%Zr alloy (abundance of 235U~20 at%) was developed, which exhibits higher radiation-induced swelling resistance than U-10 wt%Zr [9]. It is well known that DSSAs with four or more principal elements (not containing uranium), firstly proposed by Yeh et al [10] and Cantor et al [11], have drawn much attention due to their outstanding performance in phase stability [12,13], mechanical properties [14,15], and irradiation resistance [16,17]. As the perfor mance of DSSAs satisfy the demands of fuel materials, the development of uranium-bearing DSSAs may provide a feasible solution for improving fuel performance
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